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Prediction of local scour depth at bridge piers under clear-water and live-bed conditions: Comparison of literature formulae and Artificial Neural networks
UNESCO-IHE, Institute for Water Education, Westvest, 7, Delft, Netherlands.ORCID iD: 0000-0002-7575-8989
2011 (English)In: Journal of Hydroinformatics, ISSN 1464-7141, E-ISSN 1465-1734, Vol. 13, no 4, p. 812-824Article in journal (Refereed) Published
Abstract [en]

The scouring effect of the flowing water around bridge piers may undermine the stability of the structure, leading to extremely high direct and indirect costs and, in extreme cases, the loss of human lives. The use of Artificial Neural Network (ANN) models has been recently proposed in the literature for estimating the maximum scour depth around bridge piers: this study aims at further investigating the potentiality of the ANN approach and, in particular, at analysing the influence of the experimental setting (laboratory or field data) and of the sediment transport mode (clear water or live bed) on the prediction performances. A large database of both field and laboratory observations has been collected from the literature for predicting the maximum local scour depth as a function of a parsimonious set of variables characterizing the flow, the sediments and the pier. Neural networks with an increasing degree of specialization have been implemented - using different subsets of the calibration data in the training phase - and validated over an external validation dataset. The results confirm that the ANN scour depths' predictions outperform the estimates obtained by empirical formulae conventionally used in the literature and in the current engineering practice, and demonstrate the importance of taking into account the differences in the type of available data - laboratory or field data - and the sediment transport mode - clear water or live bed conditions.

Place, publisher, year, edition, pages
2011. Vol. 13, no 4, p. 812-824
Keywords [en]
bridge piers, empirical formulae, multi-layer feed forward networks, scour depth, sediment transport mode
National Category
Ocean and River Engineering
Identifiers
URN: urn:nbn:se:kth:diva-248557DOI: 10.2166/hydro.2011.065ISI: 000296242100017Scopus ID: 2-s2.0-80054998768OAI: oai:DiVA.org:kth-248557DiVA, id: diva2:1303381
Note

QC 20190626

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-06-26Bibliographically approved

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